import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()

results_folder = 'mmvec_major_taxa_scrambled_1'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'

table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()

	Propionibacteriaceae	Staphylococcus caprae or capitis	Staphylococcus epidermidis	Staphylococcus hominis	Other Staphylococci	Polyomavirus HPyV6	Polyomavirus HPyV7	Merkel Cell Polyomavirus	Malasseziaceae	Corynebacteriaceae	Micrococcaceae	Other families
featureid
X940001	0.030385	0.195576	0.445758	0.098116	-0.038128	0.110024	0.096592	0.224846	0.107593	0.190436	0.212981	0.253598
X940002	-0.030952	-0.049524	-0.063730	-0.052636	-0.025059	-0.050213	0.000735	-0.010614	-0.028183	-0.048677	-0.051594	-0.026199
X940005	-0.068787	0.051849	-0.149584	0.244480	0.030714	0.199537	0.081876	-0.019941	0.038696	0.040158	0.083170	0.004477
X940007	0.414870	0.474137	0.310297	0.606715	0.428299	0.590012	0.442074	0.366284	0.487615	0.444503	0.496156	0.417681
X940010	0.214173	0.559161	0.656579	0.715679	0.409365	0.613173	0.255437	0.322402	0.259179	0.617511	0.598442	0.375538

table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)

<seaborn.axisgrid.FacetGrid at 0x7fc084ca14d0>

sns.pairplot(table, hue='Selected')

<seaborn.axisgrid.PairGrid at 0x7fc0848733d0>

for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')